A complex dynamic system usually has a multiplicity of interacting control parameters. In order to control the dynamic system, a multiplicity of possible control actions which influence the system behavior are therefore available to a system controller. In this case, different control actions can interact in a very complex manner, in particular also in opposite directions. A control action which has a positive effect on a first control criterion may therefore have a negative effect on a second control criterion. In addition, the same control action may have a positive or negative effect depending on the system state.
Computer-aided controllers or regulators which are specifically geared to complying with or optimizing predefined control criteria are known. However, such control criteria generally differ depending on the application situation of a dynamic system. Different limit values for the exhaust gas emissions may therefore be prescribed for a power plant in different countries, for example. Whereas low exhaust gas emissions may have priority over other control criteria in a first country, low wear and low maintenance costs may have priority in a second country. A multiplicity of control criteria which influence one another should generally be weighted in a suitable manner in order to achieve control which is optimum for the present situation.
In order to optimize predefined control criteria, contemporary controllers often use machine learning techniques. For example, a neural network may be trained to optimize the control of a dynamic system with respect to one or more predefined control criteria. However, training of a neural network is generally comparatively time-consuming. If a control criterion changes, it often takes a comparatively long time for a learning neural network to adapt to the changed control criterion. It is therefore often very time-consuming to optimize suitable control criteria for an intended purpose or a new situation.